Unifying explicit and implicit feedback for top-N recommendation
Siping Liu, Xiaohan Tu, Renfa Li · 2017
In the era of big data, the data are diverse and complex. The issue that using multi-source data efficiently in recommender system is very essential. To solve this problem, we proposes a ranking model that integrates explicit feedback data with implicit feedback data together. We use weighting factors to measure the impact of different user behaviors on recommendation quality. We solved the data fusion problem and the Top-N items recommendation problem. We used matrix decomposition for collaborative filtering. Finally, a parallel optimization model based on distributed and parallel computing is proposed, and the implementation on Spark is provided. Through comparison with several models, our model greatly enhanced the items recommendation quality and improved the scalability and efficiency of personalized recommender systems.